There are numerous challenges to ensuring datasets are tagged and indexed in such a way that the datasets can be easily found by a search. For example, two dataset sources may generate tags for the two datasets using two different tagging conventions. Based on the two different tagging conventions, the two datasets may include similar types of data, but the tags may be different from each other. If the two datasets are indexed based on the different tags, a search for the two datasets may result in only one of, or neither of, the two datasets being found by the search. In view of this result, allowing similar datasets to be indexed based on the different tags may decrease the utility of searchable indexes and may lessen the reliability of searches. Moreover, as the number of datasets and the number of dataset sources increase, searching for datasets may become increasingly unreliable. Indeed, as the number of datasets and the number of dataset sources increase, the potential for differences in the tags increases. An increase in the potential for differences in tags causes search results to be less reliable.